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Alt Text: Scaling Machine Learning Models with GKE and TensorFlow
Title: Scaling Machine Learning Models with Google Kubernetes Engine and TensorFlow
Caption: Efficiently scaling ML models using GKE and TensorFlow
Description: This article explores how to efficiently scale machine learning models using Google Kubernetes Engine and TensorFlow.

International Journal of Computer Techniques – Volume 10 Issue 1, January 2023

Tulasiram Yadavalli
Role: Senior Software Engineer, USA

Abstract

Scaling machine learning (ML) models for large-scale deployment is a challenging task. Containerization using tools like Google Kubernetes Engine (GKE) and TensorFlow has emerged as a solution. This article looks at how to efficiently scale ML models using GKE alongside TensorFlow. It discusses containerized machine learning workloads, optimization strategies, and practical code-based examples. The use of GKE allows seamless orchestration of containerized applications, while TensorFlow offers powerful tools for training and inference. Together, they provide an efficient, scalable platform for deploying large ML models. The article also highlights common issues and practical solutions to improve performance and efficiency.

Keywords

machine learning, Google Kubernetes engine, TensorFlow, containerization, large-scale deployment, optimization.

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How to Cite

Independent Researcher, “Scaling Machine Learning Models with Google Kubernetes Engine and TensorFlow,” International Journal of Computer Techniques, Volume 10 Issue 1, January 2023. ISSN 2394-2231.

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